Learning Achievements in India: A Study of Primary Education in Rajasthan

Transcription

1 Learning Achievements in India: A Study of Primary Education in Rajasthan Sangeeta Goyal South Asia Human Development The World Bank Human Development Unit South Asia Region May 2007 Document of The World Bank

2 Abstract This paper presents findings from a study of learning outcomes in grades IV and V of government, private aided and private unaided schools in Rajasthan. Approximately 6000 students were tested in 200 schools in three tests two language tests (Reading Comprehension and Word Meaning) and one test in mathematics. The survey also collected information on student, family background and school characteristics. The survey results showed that overall learning levels were low absolutely and relatively in government schools. The average percentage correct scores in government schools ranged from percentage points, a quarter to a fifth below the average scores in private schools. The analysis of determinants of learning outcomes provided a number of important insights. Firstly, the school attended by the child has the most substantive impact on the quality of learning. School fixed effects account for more than half the variation in test scores. Once we take school fixed effects into account, the type of school management and other school related characteristics lose all explanatory power. Secondly, private schools, whether aided or unaided, outperform public schools. Thirdly, there is large variation in the performance of public schools - a section of public schools has better test scores than the representative private school. Findings from the study provide directions for policy interventions and for future research for more evidence-based policy making. The variation in public school performance and the dominance of school specific factors in explaining test scores imply the importance of raising quality of schools in the public sector. Moreover, not only are the learning outcomes low, learning gains from one grade to another are flat with nearly constant and large dispersion of scores around the mean in both grades. Therefore, to achieve any given learning target, improving school quality would require increasing the amount of incremental learning that takes place in each grade. Government schools also do not use their resources effectively leading to inefficient allocation of public resources to provide education. Teachers in private schools get much lower salaries than teachers in government schools and private schools are times more cost-effective than government schools in terms of learning gains per rupee. This indicates there is much room for improving the cost-effectiveness of public sector education provision. Among other determinants, we find that social group, household wealth and mother s literacy have significant but small impacts on test scores. Note: The survey work for this study was funded by the EPDF Trust Fund (Project ID: P SPN-TF054642). 2

3 1. Introduction Countries seeking to increase the level and pace of economic growth, and to raise the productivity and earnings of their citizens, have increasingly focused on increasing the quantity and quality of their people s educational attainment. Consequently, growth in school enrollment has been phenomenal across the world in the last four to five decades. However, even as the quantity of education has increased over time, the quality of education, especially primary education, remains a cause for serious concern. The experience of many developing countries including India is that children do not master basic literacy and numeracy even after four and five years of schooling. 1 In this paper we examine the determinants of learning achievements of students of grades IV and V in language and math in government, private aided and private unaided schools in Rajasthan. In India as in most developing countries, the public sector is the dominant provider of primary education. Government managed and financed primary schools are in principle freely accessible by any child of school going age. According to official data, nearly ninety percent of all primary school going children in India attend government schools. Alongside free public education, there is a growing sector of feecharging private unaided schools. These schools are managed and financed privately, often along profit-making principles. Children, even from poor families, are attending these schools in large and increasing numbers. There also exists a hybrid variety of schools in India known as government aided/private aided primary schools. These schools are managed by the private sector but largely financed by the government. 2 1 The Sarva Shiksha Abhiyan (SSA) or Universalization of Elementary Education Mission, which is the flagship mission of the Government of India in the education sector, was introduced in 2001 to ensure all eligible children go through eight years of schooling. In the first few years of the program, the focus was on improving access to schools, increasing enrollments and reducing drop-out of children from elementary grades. Increasingly, SSA is now focusing on quality of education in schools, including teacher presence and activity in class-rooms, teacher training and assessment systems. 2 All the schools that are a part of this study have recognition from the government. There is also a fast growing sector of fee-charging private unaided unrecognized schools. 3

4 According to the Census of India 2001, the western state of Rajasthan had a population of 56.5 million with a literacy rate of 60.4% as compared to the national average of 64.1%. Of the 9.96 million children in the age-group 6-14 years enrolled in school in , nearly 22% went to private schools. Historically, Rajasthan has had lower than average economic growth and human development indicators. This study is based on primary data collected in the state in early We use percentage of correctly answered questions in language and mathematics tests as proxies for cognitive competencies in literacy and numeracy, the true underlying learning objectives. Because we use percentage scored in any particular test and not acquisition of particular competencies to rank performance, the data is better suited for drawing inferences about the relative effectiveness of different determinants of learning achievements. In Section 2, we review the theoretical and empirical literature on the determinants of learning outcomes pertinent to the Indian context. In Section 3, we describe the sampling methodology, the data and the analytical framework used in this study. In Section 4, we report the unadjusted average learning levels across school types, genders, social groups and rural-urban locations. Section 5 provides the results from our empirical analyses of the determinants of learning outcomes. In Section 6, we take a look at the labor market for teachers in Rajasthan. Section 7 provides some policy implications based on the findings of this study and concludes. We will use the terms government school and public school interchangeably in this paper. 2. Background and Previous Literature The question of how to improve the quality of educational attainment in schools has become one of utmost importance to policy-makers. It is generating a large body of research, previously in developed, but now also in developing countries. Most empirical studies of determinants of learning achievement relate measurable school characteristics and student and family background characteristics to learning outcomes. 4

5 A number of studies show that school attended (school fixed effects) explains a large amount of the variation in learning outcomes. Das et al (2005) in their study of primary schools in Pakistan find that nearly 50% of all the variation in test scores in Pakistan can be attributed to school fixed effects. A study similar to this one for the state of Orissa also shows that between 50-60% of the variation in test scores is determined by school fixed effects (World Bank, forthcoming) School fixed effect plausibly captures (observable and unobservable) dimensions of school quality. Standard proxies for school quality used in the literature are school inputs such as pupil teacher ratio, the use of multi-grade classes, quantity and quality of school infrastructure, teacher numbers and characteristics, provision of mid-day-meals etc. The relation between observable schooling inputs and student outcomes however is not consistent and in general weak in most studies. Empirical evidence from developed countries generally does not find any effect of pupil-teacher ratio. Lavy and Angrist (1999) for Israel and Urqiola (2006) for rural Bolivia, however find that smaller classsizes benefits students learning attainment. Regarding the use of multi-grade classrooms, the general belief is that they are detrimental to learning though research from outside India is non-conclusive (Miller, 1990). There are few studies that include the share of graduate teachers and the share of non-regular teachers as controlling characteristics for schools. It is difficult to predict the direction of the net effects of these characteristics. Teachers with higher educational qualifications and more secure employment can be expected to be more motivated to perform. There is also evidence that they are also more prone to be more absent from schools (Chaudhury et al, 2004). The type of school management, i.e. whether the school is a government, private aided or private unaided school, has also been found to be a significant predictor of educational outcomes in the Indian context. Access to schools is a necessary but not a sufficient condition for ensuring the development of cognitive competencies. According to the empirical evidence, private unaided schools in general outperform public schools 5

6 (Kingdon, 1996; Smith et al, 2005; Tooley and Dixon, 2006). Few systematic studies compare private aided schools quality with other types. That individual student and family background characteristics influence school outcomes even after controlling for school related factors is undisputed, even though the research does not provide conclusive evidence regarding effects. Some studies find that boys and children belonging to the upper castes perform better (Dreze and Kingdon, 2001; Aggarwal, 2000; Filmer et al, 1997). Household wealth and parents education also have positive correlations with children s educational outcomes (Pritchett and Filmer, 1998). 3. Data Description and Empirical Methodology Primary data was collected for the study in the three months from February to April 2006 by A C Nielsen ORG MARG on behalf the World Bank and in cooperation with the Government of Rajasthan (GoR). Eight districts out of the 32 districts in Rajasthan were chosen for the study, in discussion with government officials, to be representative of accepted stratification of the state. Two blocks were randomly chosen from each district and 25 schools were randomly chosen from each block 12 schools from one block and 13 schools from the other. The schools were distributed across the categories of government, private aided and private unaided schools in the ratio 6:2:2. Where adequate number of private aided and unaided schools was not available, the remaining schools were replaced by government schools. Private unaided schools were restricted to those that have government recognition. The schools were distributed such that there were 8 rural schools for every 2 urban schools. A maximum of 30 students from grades IV and V were tested in each school, 15 students being randomly chosen from each grade. If more than one section of a grade was available, then first a section was chosen randomly, and then students were randomly selected from it. If fifteen or fewer children were present in a grade, then all of them were included in the sample. 6

7 Both grades IV and V students were administered the same tests in three subjects two language tests and one mathematics test. The two language tests were a reading comprehension test and a word meaning test used by the State Council of Educational Research and Training (SCERT) to test students in grade IV. The SCERT tests were the same tests used by the National Council of Educational Research and Training (NCERT). The mathematics test was a sub-sample of curriculum consistent questions selected from the TIMMS 2003 Mathematics test for grade IV. Tables 1 4 in the Annex provide descriptive statistics of the data used in this study based on a number of other questionnaires that were also administered to collect correlative information. These included: (a) School Questionnaire: This questionnaire collected information on school and teacher characteristics. (b) Student Questionnaire: This questionnaire collected information on student and family background characteristics. Analytical Framework We use a two-pronged empirical strategy to analyze the determinants of learning achievement. (1) In the first case, we use a panel approach whereby we model the achievement of student i in school j Y ij as a function of individual and family background characteristics X, a school fixed effect term z and a random error term ij j ε ij. We are able to do this because we have multiple observations from the same school. Y ij 2 = α + βx + z + ε ; where ε ~ N(0, σ ) [A] ij j ij ij This panel strategy should in principle give us unbiased and consistent estimates of individual and family characteristics. 7

8 (1) In the second case, we model the achievement of student i in school j as a Y ij function of individual and family background characteristics X ij, a vector of schooling resources S j which is constant across students from the same school, and a random error term ε ij such that Y ij 2 = α + βx + λs + ε ; where ε ~ N(0, σ ) [B] ij j ij ij We cannot have the school fixed effect and the observable school characteristics in the same equation because in that case there is likely to be perfect-collinearity. 4. Learning Levels: Differences in educational attainment by School Type, Gender, Social Group and Rural-Urban Location In this section, we provide the unadjusted learning levels of students in grades IV and V along a number of dimensions: school type, gender, social group and rural-urban location of schools. Table 4.1 below shows the means and standard deviations of the percentage scores for the three tests for grades IV and V. Students in grade IV score below 50% in Reading Comprehension and Math tests. Students in grade V have higher scores in all three tests but the difference between the subject mean scores for the two grades is always less than 10 percentage points. The average gain in grade V is highest in reading and lowest in word comprehension. 3 The dispersion in test scores, measured by the standard deviations, is very high almost 20 percentage points and is greatest for reading and lowest for the word meaning test in both grades IV and V. 3 This gain is not true gain as the set of students whose scores constitute the average are different in the two grades. 8

9 Table 4.1: Mean Scores in Tests for grades IV and V Read Word Math Percentage (%) Mean SD Mean SD Mean SD Grade IV Grade V School Type Differences: Table 4.2 shows the average percentage scores in reading comprehension, word meaning and mathematics test by grade for each of the three school types. Government school students have lower average scores than private unaided and aided school students in both grades, ranging between percentage points. Government school students score between 2 and 6 percentage points less than private unaided school students and between 9 and 16 percentage points lower than private unaided school students in grade IV. In grade V, government school students score 8-9 percentage points lower than private aided schools in all three tests, and between 8-9 percentage points lower than unaided schools in the two language tests, and about 4 percentage points lower in mathematics. Private unaided schools catch up much more with private aided schools in grade V compared to government schools. The table also shows the standard deviations of subject scores (in parentheses). Compared to the mean, the standard deviations are large across school types and across grades. For government schools, large standard deviations combined with a small mean imply that very little learning takes place for those children who are even one standard deviation below the mean. For private schools, a large standard deviation combined with a relatively large mean implies that students who score a standard deviation or more above the mean score have very high scores. Later in the paper, we will see that school specific differences account for most of the variation in test scores, and only a few percentage score points remain unexplained due to unobserved within school differences, after taking into account students background and school characteristics. This is true of variation in scores for schools within a school type and across school types. 9

10 Table 4.2: Mean Percentage Scores by School Type Mean Percentage Score (S.D.) Grade IV School Type Read Word Math Government 42.8 (22.86) (23.84) (22.53) Private Aided Private Unaided Government Private Aided Private Unaided (18.36) (19.91) (22.99) (17.76) (19.81) (19.87) (20.39) Grade V (22.12) (17.92) (21.72) (19.17) (17.44) (22.54) (19.29) (17.10) Gender Differences: There are virtually no gender differences in performance in all tests in both grades as can be seen from table 4.3 below. In fact, the unadjusted scores of girls are better than that of boys, albeit marginally. Figures 2 and 3 in the Annex also show the unadjusted scores of boys and girls by school type. In math test in grade V, girls in private aided and unaided schools score at least 5 percentage points more than boys. What is also clear from the figures is that differences in scores between school types outweigh differences in scores between boys and girls within any school type. This is true of all tests in both grades. Table 4.3: Mean Percentage Scores by Gender Mean Percentage Score Grade IV Read Word Math Boys Girls Grade V Boys Girls Social Group Differences: There are relatively larger differences in performance between children belonging to SC and ST on the one hand, and those belonging to 10

11 General and OBC categories on the other. This is true for all three tests in both grades. In grade IV, SC and ST score on the average 6-8 percentage points lower than General/OBC. In grade V, the gaps narrow between SC and others, but widen for ST. The score gaps between SC and others ranges from 3-6 percentage points and between 8-12 percentage points between ST and others. The mean scores for the different social groups by test and grade is provided in table 4.4 below. Table 4.4: Mean Percentage Scores by Social Group Mean Percentage Score Grade IV Read Word Math SC ST OBC General Grade V SC ST OBC General Figures 4 and 5 in the annex also show the average percentage scores in the three tests in the two grades by social group and school type. The figures confirm the following: OBC and General category children outperform SC and ST children in both grades IV and V, and in all three tests. The performance of SC and ST children are similar to each other and the performance of OBC and General children are similar to each other. The gaps in test scores are narrower for SC and others in grade V. For social groups, differences between school types are larger than differences between social groups in general, except in the case of mathematics scores in grade V. All students, irrespective of their social group perform worse in government schools and do better in private aided schools. 11

12 Rural-Urban Differences: Average scores are lower in rural schools in all three tests and in both grades, though the differences narrow in grade V. Rural-urban differences are highest for reading comprehension test and lowest for math. Figures 6 and 7 in the annex show the performance of schools by type and rural-urban location. From the figures, we can observe that: The greater difference is between government and private unaided schools on the one hand, and private aided schools in rural areas, on the other. Private aided schools score percentage points higher on the average than government schools and private unaided schools in rural areas. The differences in the performance of the three types of schools are smaller in urban areas. 5. Variations between Schools In Section 4, we described the unadjusted learning achievement levels and gaps by grade, gender, social group, rural-urban location and school type. We can use the method of variance-decomposition of test scores to disaggregate the total explained variation by source. The remaining which is unexplained variation can be attributed to omitted variables and noise in the data. Using this method, we can also identify the adjusted effects of particular characteristics such as school type, gender, social group etc. In a multiple regression model, the adjusted effect is the coefficient on a particular attribute, after taking into account all other characteristics. Many studies find that cognitive achievement in schools can be predicted to a large extent by the school attended. The effect of school attended can be measured by including an indicator variable for the school, i.e. by accounting for school fixed effects, using model A in Section 3 above. To determine the between and within school variations, we regress test scores on an indicator variable for the school attended. The R- square for the regression is the between variation, and the remaining is within school variation. The between and within school variation for the two grades and the three tests are shown in Figure 5.1 below. For Rajasthan, school fixed effects explain between 45% 12

13 and 72% of the variation in test scores in the two grades. For both grades IV and V, school fixed effects explains more than 70% of the test scores in mathematics and around 60% of the test scores in reading comprehension; for word meaning, school fixed effects explain between 45% - 47% of the variation. Figure 5.1: Between and Within Schools Variation by Test and Grade Between and Within Schools Variation Percentage Read Word Math Read Word Math Between Within Class IV Class V District as a source of variation in test scores by itself explains only 8-10%. Type of school management whether government, private aided or private-unaided explains between 3-4%; and the explanatory power of the grade the child attends whether grade IV or V is only 2-3%. Also, district, school type and grade effects are stronger for grade IV test scores compared to grade V. Once we take school fixed effects into account, however, district, school type and grade lose any explanatory power. We repeat the variance-decomposition exercise within each school type to compare the variations in school quality across types and between each type and the overall variation in test scores. We find that school quality within each type varies different across the tests making the interpretation of results difficult. Nevertheless, we can draw the following conclusions: 13

14 In general, government schools are the most variable, and private aided schools are the least variable in quality. However, this does not hold for mathematics where between 74 81% of the variation in test scores is explained by school attended within the group of private aided schools, more than the other two school types. The variation in school quality shrinks for grade V in private aided and unaided schools but becomes only marginally lower for government schools. Impact of School Characteristics A critical issue in analyzing educational outcomes is how schools affect learning attainment. School related factors that improve the quality of learning achievement can provide education policy options. To unpack school quality, we replace school attended in our OLS regressions by some standard measures of school quality used in empirical research. This is the second of our two-pronged empirical strategy model B in Section 3 above. We also include as a determinant the school type by management i.e. whether the school is a government, private aided or unaided school. The results are set out in columns (3) and (6) of Regression tables I, II and III in the annex for grade IV and grade V for the three tests results respectively. These results control for child and household characteristics but not for school attended. We find: In all the regressions, the coefficients on school type are in the expected direction. Private aided and unaided schools perform better than government schools. Even though, the coefficient on private unaided school is significant in only one regression word meaning test for grade V the robust t statistics are small in all the regressions. The substantive impact of school type on academic performance is not negligible, and the adjusted gap between government school and private aided and unaided schools ranges between 5-7 percentage points. Anecdotal and more systematic empirical evidence generally document that schools using multi-grade classrooms for teaching generally perform worse 14

15 than schools that don t. In the case of our data, we do not find any difference between schools on the basis of this characteristic. Schools with a higher pupil teacher ratio (PTR) perform worse in all three tests in grade IV, but no differently in tests in grade V. A 10 percentage point increase in the PTR would reduce average percentage scores by 1-2 percentage points in reading comprehension, word meaning and mathematics test scores respectively in grade IV. The average number of days of teachers absent from school in the previous academic year has a small a third to a quarter of a percentage point per day negative and significant impact, especially in grade V. A higher share of graduate teachers in the school has a small positive but non-significant effect on test scores. A higher share of teachers who are nonregular has a very small negative and non-significant effect on test scores. Overall however, the standard measures of observable school characteristics used in our analysis explain very little of the variation in test scores, and are unimportant once we take school fixed effects into account. Comparing the Distribution of Public and Private Unaided Schools Performance So far we have compared mean scores of students in different school types. Even though the typical government school performs poorly in comparison to the typical private school, there is a lot of variation in performance within the category of any particular school type. As we saw in section 5 above, most of the variation in test scores is explained by school fixed effects. Figure 10 in the annex, shows the kernel density distribution of the average school scores for reading comprehension and mathematics for government and private unaided schools. We compare only these two school types because they are pure types. The average scores for the schools have been computed by averaging individual student scores adjusted for child and family background characteristics. Apart from the density distributions, the panels also show the location of the median (the left vertical line) and the best (the right vertical line) private school. The 15

16 kernel density distributions show that not all government schools perform badly, and that there is also a substantial area of overlap between government and private unaided schools. After adjusting for student and family background characteristics, 44.44% and 60.42% of public schools perform as well or better than the median private unaided school in reading comprehension and mathematics in grade IV respectively; and 37.76% and 44.06% in grade V respectively. This is especially true of distribution of schools in grade V. From the point of view of policy, the pertinent question to ask from a policy perspective is how are good public schools different from bad public schools? For our purposes, we are simply taking the good public schools to be those that have the same or higher adjusted average scores than the median private unaided school. A comparison of the mean characteristics of the two reveals that: In grade IV, for both language and mathematics, the better public schools have a lower percentage of non-regular teachers. In grade V, for both language and mathematics, the better public schools have a higher percentage of graduate teachers. 6. The Impact of Child, Family and Social Group Characteristics As we have seen above, school attended has the maximum impact on test scores on students. However, even after controlling for school attended, observable student, family background and social group characteristics have significant, albeit relatively small effect on test scores. The regression results that identify child, family and social group characteristics effects on test scores are provided in columns (2) and (5) of Regression Tables I, II and III in the annex. In these regressions, apart from the school attended by the child, we include as determinants the child s age, gender, the child s mother s and father s literacy, whether the child lives in a rural or urban area, the social group of the child (general, SC, ST, OBC), the number of days the child was absent in the week before the interview, and 16

17 an asset index for the household the child belongs to (the construction of the asset index is described in the annex). The results of the regressions allow us to compare the unadjusted and adjusted gaps in test scores for the relevant attribute under scrutiny of the child. To reiterate, the unadjusted gap is simply the difference in average scores across an attribute such as gender or caste, whereas the adjusted gap is the coefficient of that attribute in the regression. For Rajasthan, the findings from these regressions are largely consistent with expectations a priori and with findings from other studies. We find: Age of the child in general has no impact on test scores and has a small negative impact in two of the regressions: for reading comprehension and for mathematics in grade 5, age reduces test scores between ½ and a sixth of a percentage point. Gender of the child has no or a very small and insignificant impact on test scores. Unadjusted gaps are in favor of girls in general. The adjusted gaps for girls are larger but still insignificant. This is shown in figure 7 in the annex. Social group does not seem to have a very strong impact on test scores in the case of Rajasthan, though the differences across social groups are in the right direction. Children belonging to the SC category in general score more than those belonging to ST category and less than OBC and general category children. SC children score on the average between 1 and 6 percentage points more than ST children, and between 3 and 6 percentage points less than OBC and general category children. Figures 8 and 9 in the annex show the unadjusted and adjusted gaps between the test scores of SC students and other social groups. Adjusted gaps between SC and ST children disappear and become insignificant. The adjusted gaps between SC and OBC and general categories also become much smaller and in most cases become insignificant. Only in the case of the reading comprehension test in grade IV, the adjusted gap is nearly half the adjusted gaps between test scores of SC and OBC and general category students and is significant. 17

18 We do not find any consistent impact of mother s and father s literacy on test scores. Mother s literacy matters only for reading comprehension in grade 4. Children of literate mothers on an average score 1.46 percentage points higher in the reading comprehension test in grade IV than children of illiterate mothers. Father s literacy has an impact only on mathematics score for children in grade IV. Children of literate fathers on an average score 1.33 percentage points higher in mathematics in grade 4 compared to children of illiterate fathers. The asset index of the household to which the child belongs to has either a very small, a quarter to a sixth of a percentage point, or no impact on test scores. 7. The Labor Market for Teachers Few studies analyze the characteristics of the labor market for teachers in India. In this market, the government has a near monopoly in providing qualifications and is nearly a monopsonist buyer since most teachers find employment in public sector schools. Public sector (and private aided school) teacher salaries and other benefits are set by the state through pay commissions and other political processes using considerations other than qualifications or productivity. 4 Salaries paid to teachers in private unaided schools are a fraction of those paid to government school teachers, plausibly reflecting local labor market conditions. Figure 7.1 shows the average salaries paid to regular government school teachers and teachers in private aided and unaided schools. In Rajasthan, private aided and unaided school teachers have similar average salaries, which is approximately a third less than the average salary of a regular government school teacher. 4 The variation in the salaries of regular government school teachers can largely be explained by seniority. 18

19 Figure 7.1: Average Salary of Teachers by School Type Average Salary of Regular Teachers Rupees Government Private Aided Private Unaided School Type Salary (Rupees) How do these salaries relate to differences in teacher characteristics across school types? The data shows that the distribution of teacher educational qualifications is similar between government and private unaided schools in Rajasthan. In both nearly 26% of regular teachers are non-graduates and the remaining are graduates or above. On the other hand, the share of graduates among teachers in private aided schools is on the average 7 percentage points greater. Table 7.1: Distribution of Education: Regular Teachers (%) Highest Education Government Private Private Level Aided Unaided Elementary Secondary Higher Secondary Diploma/Certificate Graduate Post-Graduate Other Total

20 Compared to government school teachers, private aided school teachers are overwhelmingly not trained. More than 70% of regular teachers in private aided schools have not received any pre-service training compared to 44% in private unaided schools and only 30% in government schools. If we look at all the teachers (regular and nonregular), then 38% in government schools, 77% in private aided schools and 57% in private unaided schools have not received any pre-service training as non-regular teachers are predominantly untrained A small set of covariates in Mincerian type wage equations years of experience, the square of years of experience, age, gender, highest educational qualification, rank, status (whether regular or otherwise) and rural-urban location explain nearly 60% of the variation in teacher salaries in each of the school type. However, the set of predictors vary across the school types. For teachers in government schools experience, status and teacher rank are the significant predictors of salary. More experienced teachers earn nearly Rupees 146 more per additional year of experience. Non-regular teachers earn nearly Rupees 5534 less than regular teachers and teachers who are not head-masters or their deputies earn Rupees 713 less than those who are. Thus experience, seniority and status are strongly correlated with teacher pay in government schools. Rural-urban location made no difference to teacher salary in government schools but in private aided schools, teachers in rural schools earn Rupees 2050 less than their urban counterparts. In private unaided schools, female teachers earn Rupees 1075 more than male teachers and non-regular teachers earn Rupees 2960 less than regular teachers. Other observable teacher characteristics were not significant predictors of salary variations in these two school types. Further analysis of teacher demographics in the various school types shows that while only a third of teachers in government and private unaided schools are females, they constitute nearly half the teaching force in private aided schools. 20

21 Teacher Incentives From the above it is clear that the representative regular teacher in a government school is relatively older, more experienced and trained compared to his or her private unaided school counterpart. Then what can account for the better performance of students in private schools over and above personal and family characteristics, which by themselves explain only very little? It is generally accepted that teacher incentives are relatively weak in government schools that leads to poor teacher performance which in turn results in poor student performance; better teacher performance in private schools, even at much lower pay, is due to the stronger structure of incentives: private school teachers can be penalized and even be fired for poor performance by school management who are in turn accountable to fee-paying parents. One aspect of teacher performance is teacher presence in schools. There is evidence of wide-spread teacher absence in government schools in India (Chaudhury et al 2004). We do not have data in our dataset that allows us to compute true absence rates for teachers. Absence information in the data-set was reported by the school respondent on a one time visit basis. The respondent was either the head teacher or a senior teacher in the school, and the data refers to the number of days in the previous school year the teacher was absent from school. On the basis of this information, teacher absence behavior was worse in private aided and unaided schools, and better in government schools. In the latter the average number of days absent (averaging by summing over all teachers) for regular teachers was 12 days compared to 24 days for private aided schools and 16 for private unaided schools. Contract teachers had similar absence behavior in government schools but non-regular teachers in private aided and unaided schools had half the number of days of absence as their regular counterparts. 8. Policy Perspective and Concluding Remarks The analysis of determinants of learning achievement in grades IV and V in Rajasthan provides important insights for the currently on-going debate on how to improve the quality of public primary education. Firstly, the school attended by the child 21

22 has the most substantive impact on the quality of learning. School fixed effects account for more than half the variation in test scores. Once we take school fixed effects into account, the type of school management loses all explanatory power. Secondly, private schools, whether aided or unaided, outperform public schools. Thirdly, there is large variation in the performance of public schools. Nearly a third of the public schools have average performance better than the median private unaided school. From the point of policy, this variation in public school performance provides the space for reforms that will enable the public schools at the bottom of the distribution to perform. Future research should explore the differences that separate the good from the bad government schools. Learning Profiles and Learning Gains What stands out from Table 4.1 above is that learning profiles are very flat: the average gain in learning in terms of percentage points from grade IV to grade V for all the students in the sample are: 7.66 in Reading Comprehension, 6.05 in Word Meaning and 6.84 in Mathematics respectively. Even if we separate out the learning gains by school type, gains are still very flat across all, particularly private aided schools where scores increase on the average by 1-4 percentage points. Average gains in government schools are between 6-8 percentage points and in private unaided schools are between 7-10 percentage points. The standard deviations of achievement scores are very high in both grades IV and V, relative to the mean. Given low mean scores, the implication is that the students who are located even one standard deviation below the mean are learning little. Moreover, there is little narrowing of the distribution of scores around the respective means in the two grades implying that the incremental learning in the higher grade is nearly constant over the entire distribution of scores. The location and shape of the distribution of test scores has implications for policy interventions aimed at improving the quality of education. Learning outcomes can be improved in at least three ways: (a) better students, (b) more school years, (c) and more learning in each grade. 22

23 (a) Better students: We can expect three types of sorting taking place that can impact on learning outcomes sorting within schools where the better ability students progress through grades, sorting across schools within a particular school type, and sorting across school types. One way to deal with the issue of selection into schools is to offer school choice by way of say school vouchers. Findings from this study show also that student characteristics as we have seen above explain little of the variation in scores, most of which is driven by school specific factors. (b) More school years: The dispersion of scores in Table 4.2 above, in each grade and relative to learning gains, is very high. If we assume a linear learning profile, then everything else remaining constant, a child in the fourth grade of a government school who is one standard deviation below the mean will take approximately six more years to reach one standard deviation above the mean in each test (Read: 46/8 6; Word: 38/6 6; Math: 40/7 6). (c) More learning in each grade: Currently, the amount of incremental learning taking place in each grade is very low. If the ideal situation is one where students on reaching grade V have mastered the intended curriculum for grade IV, then based on the findings of this study, the average shortfall (=100-Mean Score) of 50 percentage points declines only by 12-16% for the three tests in government school in the higher grade. There is little education policy can do to improve the social background of students in the long run, economic development may be the best input into the production of the quality of learning. Ensuring more school years is an untenable policy intervention because with a given quality, it will take an infeasible number of years to achieve any learning outcome target. The best option for policy makers is to steep-en the currently flat learning profile so that learning profiles in each grade approximate more closely the shape of curves in Panel (B) below. 23

24 Figure 8.1: Learning Gains in Government Schools Panel (A): Current Learning Profile Panel (B): Ideal Learning Profile Grade IV Grade V Read Word Math Grade IV Grade V Read Word Math The objectives of education policy reform needs be to (a) improve the performance of schools and (b) to keep costs down. Therefore, any education policy reform in the Indian context will have to involve teacher quality. Teachers are the main input into the teaching-learning process. Private schools perform better than public schools as is evidenced by the better performance of their students. Salaries not only constitute the largest share of the recurrent expenditures of public schools, but private school teachers earn a fraction of the salary of public school teachers. It is not the personal characteristics of the teachers but the incentives that are offered by the two school systems that plausibly determine their behavior, which in turn determines teacher quality. Most private school teachers do not have any training unlike regular public school teachers, most of whom have at least pre-service training. Public school teachers also have much higher experience in the profession on an average than private school teachers. The higher education levels of private school teachers and their choice of lowpaying employment in private schools plausibly reflects the labor market conditions. Government schools do not make use of their resources mainly teachers, effectively, and this is linked to technical or allocative inefficiency in the use of given resources. The formal condition for the optimal allocation of resources is to equalize learning gains per rupee for all inputs. In government schools, teacher salaries constitute the largest item of expenditure on school resources. The table below shows the average learning gain from grade IV to grade V by school type, divided by the average teacher salary for that school 24

25 type. For ease of interpretation, the resultant ratios (shown in columns (1), (2) and (3)) were multiplied by As can be seen in the last column in the table (column (4)), private unaided schools were nearly twice as more cost-effective than government schools, implying that public school teachers earn considerable rents. Table 8.1: Cost-Effectiveness of Education Delivery by School Type Average Learning Gain Per Rupee Government Private Aided Private Unaided (3)/(1) (1) (2) (3) (4) Read Word Math Other policy implications also emerge from the study, but by way of further research. For example, in this study we find that schools with multi-grade classrooms record lower test scores. Teachers in government schools are not trained to teach in a multi-grade classroom context. This disconnection between the realities of the teaching environment and the tools provided to teachers in government schools plausibly impacts negatively on learning outcomes. A similar case can be made regarding teaching large class-sizes which again is a reality for many government school teachers for which they may not be trained. Educational quality determines individual earnings, income distribution and economic-growth of countries (Hanushek and Woessman, 2006). Public schooling will remain the dominant provider of schooling for the majority of the population. Policy-thus makers need to find cost-effective ways to improve quality in public schools. Improving the performance of public schools is made difficult by the fact that measurable school characteristics have proven to be weak proxies for school quality in the standard education production function approach. However, there are some desirable characteristics that any reform agenda must have: Education policy reforms should be based on robust empirical evidence, given the opportunity costs of scarce public resources. Policy makers should have a fair idea about the returns to the marginal rupee across alternative interventions, and 25

26 should choose those interventions where the returns are the largest. This requires accurate assessment of the costs and benefits of any intervention. People respond to incentives. The success of any reform initiative will therefore also depend on which outcomes are identified for monitoring and evaluation, for establishing accountability and for judging success and failure of the reform. If the objective of reforms is to improve learning outcomes, then education providers line department officials, school principals, teachers and other stakeholder will have to be made accountable for achieving this goal.. 26

27 References Aggarwal, Yash (2000), Primary Education in Delhi: How Much Do The Children Learn? NIEPA, New Delhi. Chaudhury, Nazmul, Jeff Hammer, Michael Kremer, Karthik Muralidharan and F. Halsey Rogers (2004), Teacher Absence in India, The World Bank, Washington D.C. Das, Jishnu, Priyanka Pandey and Tristan Zajonc (2006), Learning Levels and Gaps in Pakistan, World Bank Policy Research Working Paper #4067, The World Bank, Washington D.C. Dreze, Jean and Geeta G. Kingdon (2001), Schooling Participation in Rural India, Review of Development Studies, 5(1), February, pp Filmer, Deon, King, Elizabeth M and Lant Pritchett (1997), Gender Disparity in South Asia: Comparison Between and Within States, World Bank Policy Research Working Paper No. 1867, The World Bank, Washington D.C. Filmer, Deon and Lant Pritchett (1998), Education Enrollment and Attainment in India: Household Wealth, Gender, Village and State Effects, South Asia Region, IDP 97, The World Bank. Fuller, Bruce (1986), Raising School Quality in Developing Countries: What Investments Boost Learning, World Bank Discussion Paper No. 2, World Bank, Washington D.C. Goldhaber, Dan, and Dominic Brewer (1997), Why Don t Schools and Teachers Seem to Matter? Assessing the Impact of Unobservables on Educational Productivity. Journal of Human Resources, 32(3), pp

28 Hanushek, Eric and L. Woessman (2006), The Role of Education Quality in Economic Growth, xx. Kingdon, G (1996), The Quality and Efficiency of Public and Private Schools: A Case Study of Urban India, Oxford Bulletin of Economics and Statistics, 58(1), February, pp Lavy, Victor and Joshua Angrist (1999), Using Maimonides Rule to Estimate the Effect of Class Size on Scholastic Achievement, Quarterly Journal of Economics, 114(2), pp Muralidharan, Karthik and Michael Kremer. Public and Private Schools in Rural India. Working Paper, Department of Economics, Harvard University, March 22, Smith, F., Hardman, F., and J. Tooley (2005), Classroom interaction and discourse in private schools serving low income families in Hyderabad, India, International Education Journal, 6(5), pp Tooley, James and Pauline Dixon (2006), De facto privatization of education and the poor: implications of a study from sub-saharan Africa and India, Compare, 36(4), pp Urqiola Miguel (2006), Identifying Class Size Effects in Developing countries: Evidence from Rural Bolivia, The Review of Economics and Statistics, 88(1), pp